MYC-deficiency impairs the development of effector/memory T lymphocytes

Authors :Mathis Nozais1*, Marie Loosveld1,2*, Saran Pankaew1, Clémence Grosjean1, Noémie Gentil1, Julie Quessada1, Cyrille Mionnet1, Delphine Potier1@ & Dominique Payet-Bornet1@

*These authors contributed equally: Mathis Nozais, Marie Loosveld; @ Corresponding authors: Dr Delphine Potier and Dr Dominique Payet-Bornet

Link to article : (TO come)

This code is made to be running on Seurat 301v2 Docker. Data and explanation about this code are available at : https://github.com/mathisnozais/MycPten

Any questions on this analysis, please contact Mathis Nozais (nozais@ciml.univ-mrs.fr) or Delphine Potier (potier@ciml.univ-mrs.fr)

[1] “You are starting the analysis from count matrix obtain with CellRanger and Cite-seq-count”

Loading the first experiment

Looking to HTO distribution accross sample :

#HTO distribution
par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto1), main = "Sequenced HTO distribution", horiz=TRUE)

rowSums(hto1)
##   Spleen-MP    Spleen-M Spleen-ctrl    Spleen-P   Thymus-MP    Thymus-M 
##      688432      206663      265412      662165      196262      117402 
## Thymus-ctrl    Thymus-P 
##      156856      562594

Demultiplexing HTO

Demultiplex cells based on HTO enrichment

Demultiplexing results

Cells classification

datatable(as.matrix(table(hashtag1@meta.data$MULTI_ID)),colnames = "Number of cells")

Violinplot (features)

VlnPlot(hashtag1,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

Violinplot (HTO counts)

VlnPlot(hashtag1,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

tSNEs based on HTO

# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag1, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag1 <- RunTSNE(hashtag1, distance.matrix = hto.dist.mtx, perplexity = 100)

HTO margin

Tsne<-data.frame(
  tSNE_1 = hashtag1@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag1@reductions$tsne@cell.embeddings[,2],
  gene= hashtag1@meta.data$HTO_margin
)

HTO= hashtag1@meta.data$MULTI_ID
Max=max(hashtag1@meta.data$HTO_margin)
Min=min(hashtag1@meta.data$HTO_margin)
ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))

Ridge plots

Visualize enrichment for selected HTOs with ridge plots

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")

Sample Information

The analysis will be run on the sample 1.

During the sample loading, we filter cells that do not pass the following filters.
Here are the description of those parameters in the Seurat CreateSeuratObject function:

  • min.genes: Include cells where at least 200 genes are detected
  • min.cells: Include genes with detected expression in at least 3 cells
a <- length(colnames(hashtag1@assays$RNA@data))
print(paste("After those filters, the remaining cell number is", a), quote = FALSE) 
## [1] After those filters, the remaining cell number is 4187
#add Exp1 cell identity
HTO_cr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-ctrl" ))
HTO_mr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-M" ))
HTO_mt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-M" ))
HTO_pr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-P" ))
HTO_pt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-P" ))
HTO_pmr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-MP" ))
HTO_pmt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-MP" ))
HTO_d1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Doublet" ))
HTO_n1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Negative" ))

HTO_thymus1 = c(HTO_ct1,HTO_mt1,HTO_pt1,HTO_pmt1)
HTO_spleen1 = c(HTO_cr1,HTO_mr1,HTO_pr1,HTO_pmr1)
HTO_identified1 = c(HTO_thymus1, HTO_spleen1)


# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset1 <- subset(x = hashtag1, cells = HTO_identified1)
VlnPlot(clean.subset1,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

a <- length(colnames(clean.subset1@assays$RNA@data))
print(paste("After removing doublets and negative cells, the remaining cell number is", a), quote = FALSE) 
## [1] After removing doublets and negative cells, the remaining cell number is 3811

Mitochondrial percentage versus nFeatures

df<-data.frame(multi.id=Seurat1@misc$old_meta_data$MULTI_ID,percent.mito=Seurat1@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat1@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())

UMAP

ggplotly(DimPlot(Seurat1, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))

Loading the second experiment

par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto2), main = "sequenced HTO distribution", horiz=TRUE)

rowSums(hto2)
##   Spleen-MP    Spleen-M Spleen-ctrl    Spleen-P   Thymus-MP    Thymus-M 
##     3569690     1668124      929963     3251282      484276      749822 
## Thymus-ctrl    Thymus-P 
##      310711     1203239

Demultiplexing results

Cells classification

datatable(as.matrix(table(hashtag2@meta.data$MULTI_ID)),colnames = "Number of cells")

Violinplot (features)

VlnPlot(hashtag2,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

Violin plots (HTO counts)

VlnPlot(hashtag2,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

tSNEs based on HTO

# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag2, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag2 <- RunTSNE(hashtag2, distance.matrix = hto.dist.mtx, perplexity = 100)
DimPlot(hashtag2, group.by = "MULTI_ID")

HTO margin

Tsne<-data.frame(
  tSNE_1 = hashtag2@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag2@reductions$tsne@cell.embeddings[,2],
  gene= hashtag2@meta.data$HTO_margin
)

HTO= hashtag2@meta.data$MULTI_ID
Max=max(hashtag2@meta.data$HTO_margin)
Min=min(hashtag2@meta.data$HTO_margin)

ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))

Ridge plots

Visualize enrichment for selected HTOs with ridge plots

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")

RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")

Sample Information

The analysis will be run on the sample 2.

During the sample loading, we filter cells that do not pass the following filters.

Used parameters in the Seurat CreateSeuratObject function: * min.genes: 3 . Include cells where at least 3 genes are detected * min.cells: 200 . Include genes with detected expression in at least 200 cells

a <-length(colnames(hashtag2@assays$RNA@data))
print(paste("After those filters, the remaining cell number is", a), quote = FALSE) 
## [1] After those filters, the remaining cell number is 12990
#add Exp2 cell identity
HTO_cr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-ctrl" ))
HTO_mr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-M" ))
HTO_mt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-M" ))
HTO_pr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-P" ))
HTO_pt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-P" ))
HTO_pmr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-MP" ))
HTO_pmt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-MP" ))
HTO_d2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Doublet" ))
HTO_n2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Negative" ))

HTO_thymus2 = c(HTO_ct2,HTO_mt2,HTO_pt2,HTO_pmt2)
HTO_spleen2 = c(HTO_cr2,HTO_mr2,HTO_pr2,HTO_pmr2)
HTO_identified2 = c(HTO_thymus2, HTO_spleen2)

# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset2 <- subset(x = hashtag2, cells = HTO_identified2)
VlnPlot(clean.subset2,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")

a <-length(colnames(clean.subset2@assays$RNA@data))
print(paste("After removing doublets and negative cells, the remaining cell number is", a), quote = FALSE) 
## [1] After removing doublets and negative cells, the remaining cell number is 10309

Adding ADT

# Load in the UMI matrix
umi <- GetAssayData(object = clean.subset2, slot = "counts")

# Load in the ADT count matrix
raw.adt <- Read10X(PATH_ADT_DATA2, gene.column = 1)
adt <- raw.adt[c(1:6),]

rownames(adt) <- c("CD4","CD5","CD8","CD25","CD44","CD69")

#create an empty matrix containing NAs
Cell.list <- colnames(GetAssayData(object = clean.subset2[["RNA"]], slot = "data" ) )
ADT.list <- c(unique(rownames(adt)))
mat.adt <- matrix(nrow = length(ADT.list), ncol = length(Cell.list))
rownames(mat.adt) = ADT.list
colnames(mat.adt) = Cell.list

# Get cell barcodes detected by both RNA and ADT
joint_bcs <- intersect(colnames(umi),colnames(adt))
adt <- as.matrix(adt[,joint_bcs])

# Fill the empty matrix with values when existing
mat.adt[,joint_bcs]<-adt[,joint_bcs]

# Add ADT data as a new assay independent from RNA
clean.subset2[["ADT"]] <- CreateAssayObject(counts = mat.adt[,colnames(clean.subset2)])

# Normalize ADT data, here we use centered log-ratio (CLR) transformation
clean.subset2 <- NormalizeData(clean.subset2, assay = "ADT", normalization.method = "CLR")

#Scale
clean.subset2 <- ScaleData(clean.subset2, assay = "ADT")

ADT list :

print (rownames(adt))

[1] “CD4” “CD5” “CD8” “CD25” “CD44” “CD69”

Mitochondrial percentage versus nFeatures

df<-data.frame(multi.id=Seurat2@misc$old_meta_data$MULTI_ID,percent.mito=Seurat2@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat2@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())

UMAP:

ggplotly(DimPlot(Seurat2, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))

Merging our two experiments

Load separate R object

You can load objects done with the code above. Or our object ?? (link )

Integrating the 2 seurat objects with seurat integration (cca)

a <- length(gene1)
b <- length(gene2)
c <- length(common_genes)

print(paste("We identified ",a," expressed in sample1 and ",b," expressed in sample2.",c,"are in common in this two set and will the integrated in the merged and corrected object."), quote = FALSE) 
## [1] We identified  13750  expressed in sample1 and  15815  expressed in sample2. 13631 are in common in this two set and will the integrated in the merged and corrected object.

UMAP:

Analysis part

Sample Information

The analysis will be run on the replicate 1 and 2.

During the sample loading, we filter cells that do not pass the following filters. We also filter cells that are detected as human/mouse multiplet using their barcodes.
Here are the description of those parameters in the Seurat CreateSeuratObject function:

  • min.genes: Include cells where at least this many genes are detected
  • min.cells: Include genes with detected expression in at least this many cells
a <- length(colnames(exp1.2.integrated))

print(paste("After those filters, and merging MYC_PTEN_01 and MYC_PTEN02 the remaining cell number is",a), quote = FALSE) 
## [1] After those filters, and merging MYC_PTEN_01 and MYC_PTEN02 the remaining cell number is 13912

UMAP and clustering parameter

Merge checking

HTO

Orig.idents

T-cell selection

According to T-cell markers we will exclude Cd3d low clusters: 13 (Bcells), 11, 14, 17 (monocytes/macrophages). According to T-cell markers we will exclude Cd3d/Cd3e low clusters: 11 (Bcells), 13, 17, 18, 19 (monocytes/macrophages), 16, 14 (ILC/NK).

Known RNA B and T markers

re clustering

T-cell umaps

HTO

clustering

END OF PREPROCESSING

We obtain the final object with clustering to start the analysis

Session Info

sessionInfo()
## R version 3.5.3 (2019-03-11)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.18.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] knitr_1.23         RColorBrewer_1.1-2 magrittr_1.5      
##  [4] dplyr_0.8.1        DT_0.6.2           gridExtra_2.3     
##  [7] kableExtra_1.1.0   plotly_4.9.0       ggplot2_3.1.1     
## [10] Seurat_3.0.1      
## 
## loaded via a namespace (and not attached):
##   [1] nlme_3.1-140        tsne_0.1-3          bitops_1.0-6       
##   [4] webshot_0.5.1       httr_1.4.0          sctransform_0.2.0  
##   [7] tools_3.5.3         R6_2.4.0            irlba_2.3.3        
##  [10] KernSmooth_2.23-15  lazyeval_0.2.2      colorspace_1.4-1   
##  [13] npsurv_0.4-0        withr_2.1.2         tidyselect_0.2.5   
##  [16] compiler_3.5.3      rvest_0.3.4         xml2_1.2.0         
##  [19] labeling_0.3        caTools_1.17.1.2    scales_1.0.0       
##  [22] lmtest_0.9-37       readr_1.3.1         ggridges_0.5.1     
##  [25] pbapply_1.4-0       stringr_1.4.0       digest_0.6.19      
##  [28] rmarkdown_1.12      R.utils_2.8.0       base64enc_0.1-3    
##  [31] pkgconfig_2.0.2     htmltools_0.3.6     bibtex_0.4.2       
##  [34] highr_0.8           htmlwidgets_1.3     rlang_0.3.4        
##  [37] rstudioapi_0.10     shiny_1.3.2         zoo_1.8-5          
##  [40] jsonlite_1.6        crosstalk_1.0.0     ica_1.0-2          
##  [43] gtools_3.8.1        R.oo_1.22.0         Matrix_1.2-17      
##  [46] Rcpp_1.0.1          munsell_0.5.0       ape_5.3            
##  [49] reticulate_1.12     R.methodsS3_1.7.1   stringi_1.4.3      
##  [52] yaml_2.2.0          gbRd_0.4-11         MASS_7.3-51.4      
##  [55] gplots_3.0.1.1      Rtsne_0.15          plyr_1.8.4         
##  [58] grid_3.5.3          promises_1.0.1      parallel_3.5.3     
##  [61] gdata_2.18.0        listenv_0.7.0       ggrepel_0.8.1      
##  [64] crayon_1.3.4        lattice_0.20-38     cowplot_0.9.4      
##  [67] splines_3.5.3       hms_0.4.2           SDMTools_1.1-221.1 
##  [70] pillar_1.4.0        igraph_1.2.4.1      future.apply_1.2.0 
##  [73] reshape2_1.4.3      codetools_0.2-16    glue_1.3.1         
##  [76] evaluate_0.13       lsei_1.2-0          metap_1.1          
##  [79] data.table_1.12.2   httpuv_1.5.1        png_0.1-7          
##  [82] Rdpack_0.11-0       gtable_0.3.0        RANN_2.6.1         
##  [85] purrr_0.3.2         tidyr_0.8.3         future_1.13.0      
##  [88] assertthat_0.2.1    xfun_0.7            mime_0.6           
##  [91] rsvd_1.0.0          xtable_1.8-4        later_0.8.0        
##  [94] survival_2.44-1.1   viridisLite_0.3.0   tibble_2.1.1       
##  [97] cluster_2.0.9       globals_0.12.4      fitdistrplus_1.0-14
## [100] ROCR_1.0-7

---
title: "Report Experiment_Preprocessing"
author: "Delphine Potier / Mathis Nozais / Saran Pankaew"
output:
  html_document:
    code_folding: hide
    code_download: true
editor_options: 
  chunk_output_type: console

---

<style type="text/css">
.main-container {
  max-width: 1800px;
  margin-left: auto;
  margin-right: auto;
}
</style>

```{r global-options, include=FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE,fig.align = 'center')
```
# MYC-deficiency impairs the development of effector/memory T lymphocytes

Authors :Mathis Nozais1\*, Marie Loosveld1,2\*, Saran Pankaew1, Clémence Grosjean1, Noémie Gentil1, Julie Quessada1, Cyrille Mionnet1, Delphine Potier1@ & Dominique Payet-Bornet1@

*These authors contributed equally: Mathis Nozais, Marie Loosveld; @ Corresponding authors: Dr Delphine Potier and Dr Dominique Payet-Bornet

Link to article : (TO come)

This code is made to be running on Seurat 301v2 Docker. Data and explanation about this code are available at : https://github.com/mathisnozais/MycPten

Any questions on this analysis, please contact Mathis Nozais (nozais@ciml.univ-mrs.fr) or Delphine Potier (potier@ciml.univ-mrs.fr)


```{r env_loading, include=FALSE}
# Load packages, data and functions
library(Seurat)
library(plotly)
library(kableExtra)
library(ggplot2)
library(gridExtra)
library(DT)

# Load the R scripts containing the functions used in the analysis
source(paste0(WORKING_DIR,"/02_Seurat_analysis/01_Script/Workflow_function.R"))

#  Path to the folder that will contain output objects
OUTPUT_PATH <- (paste0(WORKING_DIR,"/02_Seurat_analysis/02_Output/"))

# Set the random number seed
set.seed(1234)
# Resolution parameter for Seurat clustering
RESOLUTION <- 1
```

```{r, echo=FALSE,results='asis'}
SAMPLE1 <- "replicate1"
SAMPLE2 <- "replicate2"

if((! file.exists(paste0(OUTPUT_PATH, "Seurat_clean-subset_tomerge_", SAMPLE1, ".Robj"))) | (! file.exists(paste0(OUTPUT_PATH, "Seurat_clean-subset_tomerge_", SAMPLE2, ".Robj")))){
print("You are starting the analysis from count matrix obtain with CellRanger and Cite-seq-count")
part1 <- TRUE #experiment one by one
part2 <- TRUE #merging 2 object
}else if( file.exists(paste0(OUTPUT_PATH, "T-Seurat-merged_clean-subset",".Robj"))){
print("You already have the final object of preprocessing, you can now lauch the Experiment_analysis script")
part1 <- FALSE
part2 <- FALSE
}else{ 
print ("You are starting analysis from our two replicate Robj in order to do the integration")
part1 <- FALSE
part2 <- TRUE
}
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
# Loading the first experiment
```


```{r path1_loading, include=FALSE,eval=(part1 == TRUE )}
# Load path for files
PATH_MOUSE_DATA1 <- (paste0(WORKING_DIR,"/02_Seurat_analysis/03_Data/Replicate1/mRNA/"))
PROJECT_NAME1 <- paste("10X_", SAMPLE1, sep = "")
PATH_HTO_DATA1 <- (paste0(WORKING_DIR,"/02_Seurat_analysis/03_Data/Replicate1/HTO/"))
```

```{r Sample1_loading, include=FALSE,eval=(part1 == TRUE)}
# Create Seurat object and apply filtering   
# Read 10X data
mouse_data1 <- Read10X(data.dir = PATH_MOUSE_DATA1)

# Create the Seurat object and first filter
Not_processed_Seurat_m1 <- CreateSeuratObject(counts = mouse_data1, min.cells = 3, min.features = 200, project = "replicate1")
```

```{r HTO1_loading, include=FALSE,eval=(part1 == TRUE)}
# Load in the UMI matrix
umi_sparse1 <- GetAssayData(object = Not_processed_Seurat_m1, slot = "counts")

# Load in the HTO count matrix
raw.hto1 <- Read10X(PATH_HTO_DATA1, gene.column = 1)
hto1 <- raw.hto1[c(1:8),]

rownames(hto1) <- c("Spleen-MP","Spleen-M","Spleen-ctrl","Spleen-P","Thymus-MP","Thymus-M","Thymus-ctrl","Thymus-P")

# Select cell barcodes detected by both RNA and HTO
# In the example datasets we have already filtered the cells for you, but perform this step for clarity.
joint_bcs1 <- intersect(colnames(umi_sparse1),colnames(hto1))

# Subset RNA and HTO counts by joint cell barcodesumi_sparse <- pbmc_umi_sparse[,joint_bcs]
hto1 <- as.matrix(hto1[,joint_bcs1])

# Confirm that the HTO have the correct names
print (rownames(hto1))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
Looking to HTO distribution accross sample :
```

```{r HTO distribution,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
#HTO distribution
par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto1), main = "Sequenced HTO distribution", horiz=TRUE)
rowSums(hto1)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Setup seurat object and add in the hto data
# Setup Seurat object
hashtag1 <- CreateSeuratObject(counts = umi_sparse1[,joint_bcs1], assay = "RNA", project = "replicate1")

# Normalize RNA data with log normalization
hashtag1 <- NormalizeData(hashtag1,display.progress = FALSE)
# Find and scale variable genes
hashtag1 <- FindVariableFeatures(hashtag1, part1.plot = F, selection.method = "vst", nfeatures = 2000, display.progress = FALSE)
hashtag1 <- ScaleData(hashtag1,genes.use = hashtag1@var.features,display.progress = FALSE)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Adding HTO data as an independent assay

# Add HTO data as a new assay independent from RNA
hashtag1[["HTO"]] <- CreateAssayObject(counts = hto1)
hashtag1 <- SetAssayData(hashtag1,assay = "HTO",slot = "counts",new.data = hto1)
# Normalize HTO data, here we use centered log-ratio (CLR) transformation
hashtag1 <- NormalizeData(hashtag1, assay = "HTO",normalization.method = "CLR",display.progress = FALSE)
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
# Demultiplexing HTO
Demultiplex cells based on HTO enrichment
```

```{r Demultiplexing, message=FALSE, include=FALSE,eval=(part1 == TRUE)}

#Run HTOdemux just to get the HTOmax_ID fied
hashtag1 <- HTODemux(hashtag1, assay = "HTO", positive.quantile = 0.99, verbose = FALSE)
#Here we use the Seurat function MULTIseqDemux() to assign single cells back to their sample origins.
hashtag1 <- MULTIseqDemux(hashtag1, assay = "HTO",autoThresh = TRUE, maxiter = 10,qrange = seq(from = 0.1, to = 0.9, by = 0.05), verbose = TRUE)
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
##Demultiplexing results {.tabset}
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
###Cells classification
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
datatable(as.matrix(table(hashtag1@meta.data$MULTI_ID)),colnames = "Number of cells")
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violinplot (features)
```


```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag1,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violinplot (HTO counts)
```


```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag1,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## tSNEs based on HTO
```


```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag1, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag1 <- RunTSNE(hashtag1, distance.matrix = hto.dist.mtx, perplexity = 100)
```

```{r, message=FALSE,echo=FALSE,eval=(part1 == TRUE)}
DimPlot(hashtag1, group.by = "MULTI_ID",reduction = "tsne")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### HTO margin
```


```{r, fig.width = 8, fig.height = 7, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
Tsne<-data.frame(
  tSNE_1 = hashtag1@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag1@reductions$tsne@cell.embeddings[,2],
  gene= hashtag1@meta.data$HTO_margin
)

HTO= hashtag1@meta.data$MULTI_ID
Max=max(hashtag1@meta.data$HTO_margin)
Min=min(hashtag1@meta.data$HTO_margin)
ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Ridge plots

**Visualize enrichment for selected HTOs with ridge plots**
```


```{r, fig.height = 4, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag1, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
## Sample Information

The analysis will be run on the sample 1.

During the sample loading, we filter cells that do not pass the following filters.   
Here are the description of those parameters in the Seurat *CreateSeuratObject* function:
  
* min.genes: Include cells where at least 200 genes are detected
* min.cells: Include genes with detected expression in at least 3 cells

```

```{r, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
a <- length(colnames(hashtag1@assays$RNA@data))
print(paste("After those filters, the remaining cell number is", a), quote = FALSE) 
```



```{r doublet_negative_removal, results='asis',eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
#add Exp1 cell identity
HTO_cr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-ctrl" ))
HTO_mr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-M" ))
HTO_mt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-M" ))
HTO_pr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-P" ))
HTO_pt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-P" ))
HTO_pmr1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Spleen-MP" ))
HTO_pmt1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Thymus-MP" ))
HTO_d1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Doublet" ))
HTO_n1 <- row.names(subset(hashtag1@meta.data, MULTI_ID == "Negative" ))

HTO_thymus1 = c(HTO_ct1,HTO_mt1,HTO_pt1,HTO_pmt1)
HTO_spleen1 = c(HTO_cr1,HTO_mr1,HTO_pr1,HTO_pmr1)
HTO_identified1 = c(HTO_thymus1, HTO_spleen1)


# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset1 <- subset(x = hashtag1, cells = HTO_identified1)
VlnPlot(clean.subset1,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{r, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
a <- length(colnames(clean.subset1@assays$RNA@data))
print(paste("After removing doublets and negative cells, the remaining cell number is", a), quote = FALSE) 
```


```{r processing_1, include=FALSE,eval=(part1 == TRUE)}
# OBJECT ONE PROCESSING AND SAVE
#1- QC 
Seurat1 <- QC_function_mito_threshold(Seurat = clean.subset1, mito_threshold = 0.1, do_plot = FALSE)
  
#2- Find variable genes
Seurat1 <- FindVariableFeatures(object = Seurat1, 
                                  assay = "RNA", selection.method = "vst", nfeatures = 2000,
                                  verbose = FALSE, do.plot=TRUE)

Seurat1 <- ScaleData(Seurat1, 
                       assay="RNA",
                       verbose = FALSE, 
                       #do.scale = FALSE, 
                       do.center = TRUE)
  
Seurat1 <- RunPCA(object = Seurat1,
                    assay = "RNA",
                    verbose = FALSE, #if TRUE print the top genes for each PC
                    features =  VariableFeatures(object = Seurat1), 
                    seed.use = 1234,
                    npcs = 50) # sur les 50 premieres composantes
  
ElbowPlot(Seurat1, ndims = 50, reduction = "pca")
  
Seurat1 <- ProjectDim(object = Seurat1,
                        nfeatures.print = 20,
                        dims.print = 1:10)
  
Seurat1 <- RunTSNE(object = Seurat1,
                     do.fast = TRUE, 
                     seed.use = 1234,
                     dims = 1:20, # Uses 20 first PCs
                     perplexity = 40)
  
Seurat1 <- FindNeighbors(object = Seurat1, 
                           dims = 1:20 , 
                           verbose = FALSE, 
                           force.recalc = TRUE, 
                           reduction = "pca")

Seurat1 <- FindClusters(object = Seurat1, 
                          resolution = RESOLUTION,
                          verbose = FALSE,
                          random.seed = 1234)
Seurat1 <- RunUMAP(object = Seurat1, reduction = "pca", seed.use = 1234, dims = 1:20)
  
save(Seurat1, file = paste0(OUTPUT_PATH, "Seurat_clean-subset_tomerge_", SAMPLE1, ".Robj"))
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
## Mitochondrial percentage versus nFeatures
```

```{r mito_vs_nfeatures,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
df<-data.frame(multi.id=Seurat1@misc$old_meta_data$MULTI_ID,percent.mito=Seurat1@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat1@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())
```


```{asis, eval=(part1 == TRUE ), echo=TRUE}
## UMAP
```

```{r UMAP_HTO_seurat_1,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
ggplotly(DimPlot(Seurat1, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
# Loading the second experiment
```


```{r path2_loading, include=FALSE,eval=(part1 == TRUE)}
# Load path for files
PATH_MOUSE_DATA2 <- (paste0(WORKING_DIR,"/02_Seurat_analysis/03_Data/Replicate2/mRNA"))
PROJECT_NAME2 <- paste("10X_", SAMPLE2, sep = "")
PATH_HTO_DATA2 <- (paste0(WORKING_DIR,"/02_Seurat_analysis/03_Data/Replicate2/HTO"))
PATH_ADT_DATA2 <- (paste0(WORKING_DIR,"/02_Seurat_analysis/03_Data/Replicate2/ADT"))
```

```{r Sample_loading_2, include=FALSE,eval=(part1 == TRUE)}
# Create Seurat object and apply filtering   
# Read 10X data
mouse_data2 <- Read10X(data.dir = PATH_MOUSE_DATA2)


# Create the Seurat object and first filter
Not_processed_Seurat_m2 <- CreateSeuratObject(counts = mouse_data2, min.cells = 3, min.features = 200, project = "replicate2")
```

```{r , message=FALSE, include=FALSE,eval=(part1 == TRUE)}
# Load in the UMI matrix
umi_sparse2 <- GetAssayData(object = Not_processed_Seurat_m2, slot = "counts")

# Load in the HTO count matrix
raw.hto2 <- Read10X(PATH_HTO_DATA2, gene.column = 1)
hto2 <- raw.hto2[c(1:8),]

rownames(hto2) <- c("Spleen-MP","Spleen-M","Spleen-ctrl","Spleen-P","Thymus-MP","Thymus-M","Thymus-ctrl","Thymus-P")

# Select cell barcodes detected by both RNA and HTO
joint_bcs2 <- intersect(colnames(umi_sparse2),colnames(hto2))
hto2 <- as.matrix(hto2[,joint_bcs2])

# Confirm that the HTO have the correct names
print (rownames(hto2))
```

```{r,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
par(mfrow=c(1,1))
par(las=2)
par(mar=c(5,15,3,3))
barplot(rowSums(hto2), main = "sequenced HTO distribution", horiz=TRUE)

rowSums(hto2)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Setup seurat object and add in the hto data
# Setup Seurat object
hashtag2 <- CreateSeuratObject(counts = umi_sparse2[,joint_bcs2 ], assay = "RNA", project = "replicate2")

# Normalize RNA data with log normalization
hashtag2 <- NormalizeData(hashtag2,display.progress = FALSE)
# Find and scale variable genes
hashtag2 <- FindVariableFeatures(hashtag2,do.plot = F,selection.method = "vst", nfeatures = 2000, display.progress = FALSE)
hashtag2 <- ScaleData(hashtag2,genes.use = hashtag2@assays$RNA@var.features,display.progress = FALSE)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
### Adding HTO data as an independent assay
# Add HTO data as a new assay independent from RNA
hashtag2[["HTO"]] <- CreateAssayObject(counts = hto2)
hashtag2 <- SetAssayData(hashtag2,assay = "HTO",slot = "counts",new.data = hto2)
# Normalize HTO data, here we use centered log-ratio (CLR) transformation
hashtag2 <- NormalizeData(hashtag2, assay = "HTO",normalization.method = "CLR",display.progress = FALSE)
```

```{r, message=FALSE, include=FALSE,eval=(part1 == TRUE)}
#Run HTOdemux just to get the HTO_maxID field
hashtag2 <- HTODemux(hashtag2, assay = "HTO", positive.quantile = 0.99, verbose = FALSE)
#Demultiplex cells based on HTO enrichment
hashtag2 <- MULTIseqDemux(hashtag2, assay = "HTO",autoThresh = TRUE, maxiter = 10,qrange = seq(from = 0.1, to = 0.9, by = 0.05), verbose = TRUE)
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
##Demultiplexing results {.tabset}
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
###Cells classification
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
datatable(as.matrix(table(hashtag2@meta.data$MULTI_ID)),colnames = "Number of cells")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violinplot (features)
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag2,features = c("nFeature_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Violin plots (HTO counts)
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
VlnPlot(hashtag2,features = c("nCount_HTO"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## tSNEs based on HTO
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
# Calculate a distance matrix using HTO
hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = hashtag2, assay = "HTO"))))

# Calculate tSNE embeddings with a distance matrix
hashtag2 <- RunTSNE(hashtag2, distance.matrix = hto.dist.mtx, perplexity = 100)
```

```{r, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
DimPlot(hashtag2, group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### HTO margin
```


```{r, fig.width = 8, fig.height = 7, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
Tsne<-data.frame(
  tSNE_1 = hashtag2@reductions$tsne@cell.embeddings[,1],
  tSNE_2= hashtag2@reductions$tsne@cell.embeddings[,2],
  gene= hashtag2@meta.data$HTO_margin
)

HTO= hashtag2@meta.data$MULTI_ID
Max=max(hashtag2@meta.data$HTO_margin)
Min=min(hashtag2@meta.data$HTO_margin)

ggplot(Tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(color=gene,shape=HTO))+
           scale_colour_gradient2(low = "blue",mid="orange",high="red",name="HTO margin",midpoint=(Max+Min)/2)+scale_shape_manual(values = c(15,16,17,18,19,20,21,22,23,24,25))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
### Ridge plots

**Visualize enrichment for selected HTOs with ridge plots**
```


```{r, fig.height = 4, message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[1:2],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[3:4],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[5:6],ncol = 2, group.by = "MULTI_ID")
RidgePlot(hashtag2, assay = "HTO", features = rownames(GetAssayData(hashtag1,assay = "HTO"))[7:8],ncol = 2, group.by = "MULTI_ID")
```

```{asis, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}

## Sample Information

The analysis will be run on the sample 2.

During the sample loading, we filter cells that do not pass the following filters.

Used parameters in the Seurat *CreateSeuratObject* function:
* min.genes: 3 . Include cells where at least 3 genes are detected
* min.cells: 200 . Include genes with detected expression in at least 200 cells
```

```{r, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
a <-length(colnames(hashtag2@assays$RNA@data))
print(paste("After those filters, the remaining cell number is", a), quote = FALSE) 
```

```{r cell_select_2, results='asis',eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
#add Exp2 cell identity
HTO_cr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID == "Spleen-ctrl" ))
HTO_ct2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-ctrl" ))
HTO_mr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-M" ))
HTO_mt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-M" ))
HTO_pr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-P" ))
HTO_pt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-P" ))
HTO_pmr2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Spleen-MP" ))
HTO_pmt2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Thymus-MP" ))
HTO_d2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Doublet" ))
HTO_n2 <- row.names(subset(hashtag2@meta.data, MULTI_ID== "Negative" ))

HTO_thymus2 = c(HTO_ct2,HTO_mt2,HTO_pt2,HTO_pmt2)
HTO_spleen2 = c(HTO_cr2,HTO_mr2,HTO_pr2,HTO_pmr2)
HTO_identified2 = c(HTO_thymus2, HTO_spleen2)

# Create a Seurat object without doublet and unassigned cells (remove "negative", "doublet" & "nothing"))
clean.subset2 <- subset(x = hashtag2, cells = HTO_identified2)
VlnPlot(clean.subset2,features = c("nFeature_RNA", "nCount_RNA"),pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
```

```{r, eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
a <-length(colnames(clean.subset2@assays$RNA@data))
print(paste("After removing doublets and negative cells, the remaining cell number is", a), quote = FALSE) 
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## Adding ADT
```


```{r load_adt,  message=FALSE,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
# Load in the UMI matrix
umi <- GetAssayData(object = clean.subset2, slot = "counts")

# Load in the ADT count matrix
raw.adt <- Read10X(PATH_ADT_DATA2, gene.column = 1)
adt <- raw.adt[c(1:6),]

rownames(adt) <- c("CD4","CD5","CD8","CD25","CD44","CD69")

#create an empty matrix containing NAs
Cell.list <- colnames(GetAssayData(object = clean.subset2[["RNA"]], slot = "data" ) )
ADT.list <- c(unique(rownames(adt)))
mat.adt <- matrix(nrow = length(ADT.list), ncol = length(Cell.list))
rownames(mat.adt) = ADT.list
colnames(mat.adt) = Cell.list

# Get cell barcodes detected by both RNA and ADT
joint_bcs <- intersect(colnames(umi),colnames(adt))
adt <- as.matrix(adt[,joint_bcs])

# Fill the empty matrix with values when existing
mat.adt[,joint_bcs]<-adt[,joint_bcs]

# Add ADT data as a new assay independent from RNA
clean.subset2[["ADT"]] <- CreateAssayObject(counts = mat.adt[,colnames(clean.subset2)])

# Normalize ADT data, here we use centered log-ratio (CLR) transformation
clean.subset2 <- NormalizeData(clean.subset2, assay = "ADT", normalization.method = "CLR")

#Scale
clean.subset2 <- ScaleData(clean.subset2, assay = "ADT")
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
ADT list :
```


```{r , results='asis',eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
print (rownames(adt))
```

```{r Significant_PC, include=FALSE,eval=(part1 == TRUE)}
# OBJECT TWO PROCESSING AND SAVE
#1- QC 
Seurat2 <- QC_function_mito_threshold(Seurat = clean.subset2, mito_threshold = 0.1, do_plot = FALSE)
  
#2- Find variable genes
Seurat2 <- FindVariableFeatures(object = Seurat2, 
                                  assay = "RNA",
                                  selection.method = "vst", nfeatures = 2000, verbose = FALSE, do.plot=TRUE)
  
Seurat2 <- ScaleData(Seurat2, 
                       assay="RNA",
                       verbose = FALSE, 
                       #do.scale = FALSE, 
                       do.center = TRUE)
  
Seurat2 <- RunPCA(object = Seurat2,
                    assay = "RNA",
                    verbose = FALSE, #if TRUE print the top genes for each PC
                    features =  VariableFeatures(object = Seurat2), 
                    seed.use = 1234,
                    npcs = 50) 
  
ElbowPlot(Seurat2, ndims = 40, reduction = "pca")
  
Seurat <- ProjectDim(object = Seurat2,
                       nfeatures.print = 20,
                       dims.print = 1:10)

Seurat2 <- RunTSNE(object = Seurat2,
                     do.fast = TRUE, 
                     seed.use = 1234,
                     dims = 1:20, 
                     perplexity = 40)
  
Seurat2 <- FindNeighbors(object = Seurat2, 
                         dims = 1:20 , 
                           verbose = FALSE, 
                           force.recalc = TRUE, 
                           reduction = "pca")
  
Seurat2 <- FindClusters(object = Seurat2, 
                          resolution = RESOLUTION,
                          verbose = FALSE,
                          random.seed = 1234)
  
Seurat2 <- RunUMAP(object = Seurat2, reduction = "pca", seed.use = 1234, dims = 1:20)
  
save(Seurat2, file = paste0(OUTPUT_PATH, "Seurat_clean-subset_tomerge_", SAMPLE2, ".Robj"))
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## Mitochondrial percentage versus nFeatures
```

```{r mito_vs_nfeatures2,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
df<-data.frame(multi.id=Seurat2@misc$old_meta_data$MULTI_ID,percent.mito=Seurat2@misc$old_meta_data$percent.mito,nFeature_RNA=Seurat2@misc$old_meta_data$nFeature_RNA)
ggplotly(ggplot(df,aes(x=nFeature_RNA,y=percent.mito,color=multi.id))+geom_point())
```

```{asis, eval=(part1 == TRUE ), echo=TRUE}
## UMAP:
```

```{r UMAP_HTO_seurat_2,eval=(part1 == TRUE ), echo=if (part1) TRUE else FALSE}
ggplotly(DimPlot(Seurat2, reduction = "umap", group.by = "MULTI_ID", do.label = TRUE, pt.size = 1))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
#Merging our two experiments
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## Load separate R object
You can load objects done with the code above.
Or our object ?? (link )
```

```{r Samples_loading,eval=(part2 == TRUE ) ,include=FALSE}
#load all seurat objects built previously
load(paste0(OUTPUT_PATH, "Seurat_clean-subset_tomerge_", SAMPLE1, ".Robj"))
load(paste0(OUTPUT_PATH, "Seurat_clean-subset_tomerge_", SAMPLE2, ".Robj"))
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## Integrating the 2 seurat objects with seurat integration (cca)
```

```{r, CCA, eval=(part2 == TRUE ),include=FALSE}
# Gene selection for input to CCA
FindVariableFeatures(object = Seurat1, 
        selection.method = "vst", nfeatures = 2000, verbose = FALSE)
FindVariableFeatures(object = Seurat2, 
        selection.method = "vst", nfeatures = 2000, verbose = FALSE)

exp.anchors <- FindIntegrationAnchors(object.list = c(Seurat2,Seurat1), dims = 1:30)

gene1 <- rownames(GetAssayData(Seurat1, assay = "RNA", slot = "data" ))
gene2 <- rownames(GetAssayData(Seurat2, assay = "RNA", slot = "data" ))
common_genes <- Reduce(intersect, list(gene1,gene2))

exp1.2.integrated <- IntegrateData(anchorset = exp.anchors, features.to.integrate = common_genes,dims = 1:30)
```

```{r, eval=(part2 == TRUE ), echo=TRUE}
a <- length(gene1)
b <- length(gene2)
c <- length(common_genes)

print(paste("We identified ",a," expressed in sample1 and ",b," expressed in sample2.",c,"are in common in this two set and will the integrated in the merged and corrected object."), quote = FALSE) 

```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
## UMAP:

# Analysis part

## Sample Information

The analysis will be run on the replicate 1 and 2.

During the sample loading, we filter cells that do not pass the following filters. We also filter cells that are detected as human/mouse multiplet using their barcodes.  
Here are the description of those parameters in the Seurat *CreateSeuratObject* function:

* min.genes: Include cells where at least this many genes are detected
* min.cells: Include genes with detected expression in at least this many cells

```

```{r filters, eval=(part2 == TRUE ),results='asis',include=FALSE}
# Affiche les parametres
Filter_parameters <- data.frame()
Filter_parameters["Value", "min.cells"] <- 3
Filter_parameters["Value", "min.genes"] <- 200
kable(Filter_parameters, "html", align = "c") %>% kable_styling(bootstrap_options = c("striped", "hover"))
```

```{r, eval=(part2 == TRUE ), echo=TRUE}
a <- length(colnames(exp1.2.integrated))

print(paste("After those filters, and merging MYC_PTEN_01 and MYC_PTEN02 the remaining cell number is",a), quote = FALSE) 

```


```{r cell_select,eval=(part2 == TRUE ), results='asis',include=FALSE}
### add Exp1 cell identity
#add Exp1 cell identity
HTO_cr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-ctrl" )),"_2"),colnames(x = exp1.2.integrated))
HTO_ct6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-ctrl" )),"_2"),colnames(x = exp1.2.integrated))
HTO_mr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-M" )),"_2"),colnames(x = exp1.2.integrated))
HTO_mt6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-M" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-P" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pt6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-P" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pmr6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Spleen-MP" )),"_2"),colnames(x = exp1.2.integrated))
HTO_pmt6 <- intersect(paste0(row.names(subset(Seurat1@meta.data, MULTI_ID == "Thymus-MP" )),"_2"),colnames(x = exp1.2.integrated))

### add Exp2 cell identity
HTO_cr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID == "Spleen-ctrl" )),"_1"),colnames(x = exp1.2.integrated))
HTO_ct2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-ctrl" )),"_1"),colnames(x = exp1.2.integrated))
HTO_mr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Spleen-M" )),"_1"),colnames(x = exp1.2.integrated))
HTO_mt2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-M" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Spleen-P" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pt2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-P" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pmr2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Spleen-MP" )),"_1"),colnames(x = exp1.2.integrated))
HTO_pmt2 <- intersect(paste0(row.names(subset(Seurat2@meta.data, MULTI_ID== "Thymus-MP" )),"_1"),colnames(x = exp1.2.integrated))



exp1.2.integrated@meta.data$HTO = "nothing"
exp1.2.integrated@meta.data[HTO_cr2,]$HTO = "WT spleen"
exp1.2.integrated@meta.data[HTO_ct2,]$HTO = "WT thymus"
exp1.2.integrated@meta.data[HTO_cr6,]$HTO = "WT spleen"
exp1.2.integrated@meta.data[HTO_ct6,]$HTO = "WT thymus"
exp1.2.integrated@meta.data[HTO_pr2,]$HTO = "PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pt2,]$HTO = "PTEN- thymus"
exp1.2.integrated@meta.data[HTO_pr6,]$HTO = "PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pt6,]$HTO = "PTEN- thymus"
exp1.2.integrated@meta.data[HTO_mr2,]$HTO = "MYC- spleen"
exp1.2.integrated@meta.data[HTO_mt2,]$HTO = "MYC- thymus"
exp1.2.integrated@meta.data[HTO_mr6,]$HTO = "MYC- spleen"
exp1.2.integrated@meta.data[HTO_mt6,]$HTO = "MYC- thymus"
exp1.2.integrated@meta.data[HTO_pmr2,]$HTO = "Myc- PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pmt2,]$HTO = "Myc- PTEN- thymus"
exp1.2.integrated@meta.data[HTO_pmr6,]$HTO = "Myc- PTEN- spleen"
exp1.2.integrated@meta.data[HTO_pmt6,]$HTO = "Myc- PTEN- thymus"

HTO_thymus = c(HTO_ct2,HTO_mt2,HTO_pt2,HTO_pmt2,HTO_ct6,HTO_mt6,HTO_pt6,HTO_pmt6)
HTO_spleen = c(HTO_cr2,HTO_mr2,HTO_pr2,HTO_pmr2,HTO_cr6,HTO_mr6,HTO_pr6,HTO_pmr6)

identified <- c(HTO_thymus,HTO_spleen)
VlnPlot(exp1.2.integrated,features = "nFeature_RNA",pt.size = 0.1, log = TRUE,  group.by = "MULTI_ID")
VlnPlot(exp1.2.integrated,features = "nFeature_RNA",pt.size = 0.1, log = TRUE,  group.by = "HTO")
VlnPlot(exp1.2.integrated,features = "nFeature_RNA",pt.size = 0.1, log = TRUE,  group.by = "orig.ident")

Seurat <- exp1.2.integrated
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## UMAP and clustering parameter
```

```{r Significant_PC_merge, eval=(part2 == TRUE ),include=FALSE}
# Traitement de l'objet
Seurat <- ScaleData( object =  Seurat, 
                      assay="integrated",
                      verbose = FALSE,
                      #do.scale = FALSE,
                      do.center = TRUE)
  
Seurat <- RunPCA(object = Seurat, features = VariableFeatures(Seurat), npcs = 50, seed.use = 1234, verbose = FALSE)
  
ElbowPlot(Seurat, ndims = 40, reduction = "pca")
  
Seurat <- ProjectDim(object = Seurat,
                  assay="integrated",
                  nfeatures.print = 20,
                  dims.print = 1:12)
  
Seurat <- FindNeighbors(object = Seurat, 
                  dims = 1:12 , 
                  assay="integrated",
                  verbose = FALSE)#, 
                  #force.recalc = TRUE, 
                  #reduction = "pca")
  
Seurat <- FindClusters(object = Seurat, 
                  resolution = 1,
                  assay="integrated",
                  verbose = FALSE,
                  random.seed = 1234)
    
  #To make the UMAP
  #######################
Seurat <- RunUMAP(object = Seurat, reduction = "pca", seed.use = 1234, dims = 1:12)
  
DimPlot(object = Seurat, reduction = "umap", group.by = "orig.ident")
  
save(Seurat, file = paste0(OUTPUT_PATH, "Seurat-integrated_rep1_rep2.Robj"))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
## Merge checking {.tabset}
### HTO
```

```{r,eval=(part2 == TRUE ), echo=FALSE}
ggplotly(DimPlot(Seurat, reduction = "umap", group.by = "HTO", do.label = TRUE, pt.size = 1)+
           ggtitle("UMAP colorred by HTO classification"))
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
### Orig.idents
```

```{r, eval=(part2 == TRUE ),echo=FALSE}
ggplotly(DimPlot(Seurat, reduction = "umap", group.by = "orig.ident", do.label = TRUE, pt.size = 1))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
## T-cell selection {.tabset}
According to T-cell markers we will exclude Cd3d low clusters: 13 (Bcells), 11, 14, 17 (monocytes/macrophages).
According to T-cell markers we will exclude Cd3d/Cd3e low clusters: 11 (Bcells), 13, 17, 18, 19 (monocytes/macrophages), 16, 14 (ILC/NK).

### Known RNA B and T markers
```

```{r bt_markers_checking, eval=(part2 == TRUE ), echo=FALSE}
DimPlot(Seurat, label = T)
bcell_known_markers <- c("Cd74","Ms4a1","Cd19","Cd3d")
FeaturePlot(object = Seurat, features = bcell_known_markers, reduction = "umap",  cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
bcell_known_markers <- c("Cd14","Fcgr3","Trdc","Cd3d")
FeaturePlot(object = Seurat, features = bcell_known_markers, reduction = "umap",  cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
FeaturePlot(object = Seurat, features = c("Il2ra","Klrg1","Il7r","Rora"), reduction = "umap", order = TRUE, cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
FeaturePlot(object = Seurat, features = c("Eomes","Ncr1","Tbx21","Kit"), reduction = "umap", order = TRUE, cols = c("grey", "light blue","cyan3","cyan4","dodgerblue3","blue","mediumslateblue","purple","orchid3","red","brown","black"))
#Nrc1 = NK
#IL7R to separate ILC from NK; IL7R+ EOMES+ should be ILC1; EOMES+ IL7R- should be NK
```

```{r Tcell_selection, eval=(part2 == TRUE ),echo=FALSE}
#remove cluster 11,12,13,17 (#Bcells & macrophages)
T.Seurat <- subset(x = Seurat, idents = c("11","13","14","16","17","18","19"), invert = TRUE)
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## re clustering
```

```{r, include=FALSE, eval=(part2 == TRUE )}
# INTEGRATE OBJECT AND SAVE

# Selection was already done at the integration step, even after resubsetting it is worth to reselect (https://github.com/satijalab/seurat/issues/1528). RNA should be used, but then batch is back, subsetting before integration is not good neither (better to keep cells that can be aligned)... 
  
T.Seurat <- ScaleData( object =  T.Seurat, 
                      assay="integrated",
                      verbose = FALSE,
                      #do.scale = FALSE,
                      do.center = TRUE)
  
T.Seurat <- RunPCA(object = T.Seurat, features = VariableFeatures(T.Seurat), npcs = 100, seed.use = 1234, verbose = FALSE)
  
ElbowPlot(T.Seurat, ndims = 50, reduction = "pca")

T.Seurat <- ProjectDim(object = T.Seurat,
                  nfeatures.print = 20,
                  dims.print = 1:10)
  
T.Seurat <- FindNeighbors(object = T.Seurat,
                  assay = "integrated",
                  dims = 1:18 , 
                  verbose = FALSE)#, 
                  #force.recalc = TRUE, 
                  #reduction = "pca")
  
  T.Seurat <- FindClusters(object = T.Seurat, 
                  assay = "integrated",
                  resolution = 1.8,
                  verbose = FALSE,
                  random.seed = 1234)
  #To make the UMAP
  #######################
  T.Seurat <- RunUMAP(object = T.Seurat, reduction = "pca", seed.use = 1234, dims = 1:18)
  
  DimPlot(object = T.Seurat, reduction = "umap", group.by = "orig.ident")
  p1 <- DimPlot(object = T.Seurat, reduction = "umap", group.by = "orig.ident")
  p2 <- DimPlot(object = T.Seurat, reduction = "pca", group.by = "orig.ident", 
      label = TRUE, repel = TRUE) + NoLegend()
  
  grid.arrange(p1,p2,nrow = 1, ncol =2, newpage=TRUE)

  save(T.Seurat, file = paste0(OUTPUT_PATH, "T-Seurat-merged_clean-subset", ".Robj"))
```

```{r , eval=(part2 == TRUE ),echo=FALSE}
### add Exp2 cell identity 
spleen.cells <- c(row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-ctrl" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-M" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-MP" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Spleen-P" )))

thymus.cells <- c(row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-ctrl" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-M" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-MP" )),row.names(subset(T.Seurat@meta.data, MULTI_ID == "Thymus-P" )))

T.Seurat@meta.data$tissue = "nothing"
T.Seurat@meta.data[spleen.cells,]$tissue = "Spleen"
T.Seurat@meta.data[thymus.cells,]$tissue = "Thymus"

DimPlot(T.Seurat)
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
## T-cell umaps {.tabset}
### HTO
```

```{r, eval=(part2 == TRUE ),echo=FALSE}
ggplotly(DimPlot(T.Seurat, group.by = "MULTI_ID"))
```


```{asis, eval=(part2 == TRUE ), echo=TRUE}
### clustering
```

```{r, eval=(part2 == TRUE ),echo=FALSE}
(DimPlot(T.Seurat, reduction = "umap", group.by = "integrated_snn_res.1.8", label = TRUE, pt.size = 1))
```

```{asis, eval=(part2 == TRUE ), echo=TRUE}
## END OF PREPROCESSING
We obtain the final object with clustering to start the analysis
```

<details>
  <summary>**Session Info**</summary>
```{r}
sessionInfo()
```
</details>
